To address these challenges, the ER3 framework is proposed to simultaneously achieve event retrieval, recognition and recounting. Figure REF  illustrates the components and the inputs/outputs of such ER3 system.  In ER3, (i) we introduce a feature alignment step which can significantly suppress the redundant information and generate a more comprehensive and compact video representation called video imprint. In addition, the video imprint also preserves the local spatial layout among video frames. (ii) Based on the video imprint, in unsupervised setting, we propose an efficient aggregation method for large-scale event retrieval. In supervised setting, we further employ a reasoning network, a modified version of the neural memory networks [1]}, which can simultaneously recognize the event category and locate the key pieces of evidence for the event category. In fact, the recounting is so naturally integrated in the framework that the experiments show that the recounting step can assist the recognition task and improve the recognition accuracy. (iii) In the recounting task, both temporal key frame identification (attribution of the key frames with respect to the event category, as in [2]}, [3]}) and spatial key areas localization (attribution of the key areas within each frame) are implemented, thanks to the video imprint preserving local semantic and spatial layouts.
